Pandas Series.mean() Function

In Pandas, the Series.mean() function is used to compute the mean or average value of the elements within a Series. Upon execution, it yields a single float value, signifying the calculated mean of the series.

Pandas Series.mean() Introduction

Following is the syntax of creating Series.mean() function.


# Syntax of Series.mean() function
Series.mean(axis=_NoDefault.no_default, skipna=True, level=None, numeric_only=None, **kwargs)

Parameters of the series.mean() Function

Following are the parameters of the mean().

axis – {0 or ‘index’, 1 or ‘columns’}, default None. Axis for the function to be applied on. If 0 or ‘index’, it computes the mean along the index (rows); if 1 or ‘columns’, it computes the mean along columns.
skipna – bool, default None. If True, NA/null values are excluded from the calculation. If False, NA/null values are included in the calculation. If None, it is automatically set to True unless at least one NA/null value is present.
level – int or level name, default None. If the axis is a MultiIndex (hierarchical), the level for which the mean is calculated.
numeric_only – bool, default None. If True, only numeric types are included in the calculation. If False, all data types are included.
**kwargs – Additional keyword arguments are passed to the function that performs the actual calculation.

Return Value

It returns a single float value representing the mean of the elements in the Series.

Pandas Series mean() Usage

The mean() function returns the arithmetic mean of given object elements in Pandas. Arithmetic mean is a sum of elements of given object, along with the specified axis divided by the number of elements.

You can also specify the axis parameter to specify the axis along which the mean is calculated. By default, axis=0, which means the mean is calculated along the rows (i.e., across all the columns). If you set axis=1, the mean is calculated along the columns (i.e., across all the rows).

To run some examples of pandas series mean function, let’s create pandas series.


import pandas as pd
import numpy as np

# Create a Series
ser = pd.Series([13, 25, 6, 10, 12, 9, 20])
print(ser)

The following example calculates the mean.


# Use Series.mean() function
ser2 = ser.mean()
print(ser2)

# Output:
# 13.571428571428571

Series Mean Ignore NaN

By default, skipna=True, which means NaN (Not a Number) values are ignored when calculating the mean. If a series contains NaN values, they are automatically excluded from the calculation.


# Pandas series mean ignore nan
ser = pd.Series([13, 25, None, 10, 12, None, 20, 30, np.nan])
ser2 = ser.mean(skipna = True)
print(ser2)

# Output:
# 18.333333333333332

Similarly, you can also set skipna=False to include NaN values in the calculation. If your Series contains NaN values and you choose not to skip them, the mean calculation will return NaN (Not a Number) as the result.


# Pandas series mean ignore nan
ser = pd.Series([13, 25, None, 10, 12, None, 20, 30, np.nan])
ser2 = ser.mean(skipna = False)
print(ser2)

# Output:
# nan

Complete Example of Series.mean() Function


import pandas as pd
import numpy as np

# Create a Series
ser = pd.Series([13, 25, 6, 10, 12, 9, 20])
print(ser)

# Use Series.mean() function
ser2 = ser.mean()
print(ser2)

# Pandas series mean ignore nan
ser = pd.Series([13, 25, None, 10, 12, None, 20, 30, np.nan])
ser2 = ser.mean(skipna = True)
print(ser2)

Conclusion

In this article, you have learned the mean() function and using its syntax, parameters, and usage for computing the mean or average value of a given Series object.

Happy Learning !!

References

https://pandas.pydata.org/docs/reference/api/pandas.Series.mode.html